@le-and-er.bsky.social
Postdoc between Helmholtz Munich and ETH Zurich | Theis Lab & Treutlein Lab | Computational Biology, Machine Learning & Organoids
Happy to share CellAnnotator, a lightweight Python package to query OpenAI models for initial cell type annotations: github.com/quadbio/cell...
Inspired by ideas in www.nature.com/articles/s41... and github.com/VPetukhov/GP..., implemented as an scVerse ecosystem package with docs and tutorials.
From cell lines to full embryos, drug treatments to genetic perturbations, neuron engineering to virtual organoid screens β odds are thereβs something in it for you!
Built on flow matching, CellFlow can help guide your next phenotypic screen: biorxiv.org/content/10.1101/2025.04.11.648220v1
Our paper benchmarking feature selection for scRNA-seq integration and reference usage is out now www.nature.com/articles/s41...!
Keep reading for more about how we did the study and what we found out 𧡠π
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β‘οΈOur findings have implications for understanding how prenatal stressors can shape brain function and contribute to mental health disorders later in life. A critical step toward bridging the gap between genetics and environment in brain health.
Check out the press release β¬οΈ
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β οΈ Why it matters: We show how environmental factors, like GC exposure, can converge to affect brain development through common molecular mechanisms as genetic risk factors: priming of the inhibitory neuron lineage.
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We identified PBX3 as an example of a TF that is closely linked to the inhibitory neuron lineage priming. In silico perturbation experiments of multimodal GRNs suggest PBX3 as a potential mediator of the effect. π§¬π
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Our findings show that chronic GC exposure alters lineage specification, with a selective priming of the inhibitory neuron lineage. π§
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In our study, we looked at how chronic GC exposure affects neural differentiation and lineage specification in human neural organoids, using single-cell techniques to map the molecular changes in detail. π¬π§¬
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π¨π§ New paper alert: Stress alters neuronal balance in the developing brain π§ π¨
Our latest study in @science.orgAdvances explores how glucocorticoid exposureβa key environmental risk factorβshapes early human brain development using #organoids.
@mpi-psychiatry.bsky.social @drcricru.bsky.social
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Insightful study by L. Dony @anthikrontira.bsky.social @drcricru.bsky.social @binderlabmpip.bsky.social @silvianeuro.bsky.social @fabiantheis.bsky.social on the effects of chronic exposure to glucocorticoids for neurodevelopment. Organoid model reveals amplification of inhibitory neuron cell fate π§ͺ
15.02.2025 08:59 β π 14 π 6 π¬ 1 π 0
When applying machine learning to human health data, it is not enough to just improve a metric by another percent. We have to go deeper. In our perspective in Nature Cell Biology, we discuss caveats and biases of human single-cell data analysis: nature.com/articles/s41...
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Good to see moscot-tools.org published in @nature.com ! We made existing Optimal Transport (OT) applications in single-cell genomics scalable and multimodal, added a novel spatiotemporal trajectory inference method and found exciting new biology in the pancreas! tinyurl.com/33zuwsep
23.01.2025 08:41 β π 49 π 13 π¬ 1 π 3
If diving into scientific papers isnβt on your pre-Christmas to-do list, @spiegel.de has you covered! π° They featured our Neural Organoid Atlas in a piece on the Human Cell Atlas and related advancements in machine learning. π€π§¬ Itβs a great overview of this exciting field and the work behind it.
19.12.2024 16:05 β π 10 π 7 π¬ 0 π 0
And of course to our amazing PIs @graycamplab.bsky.social, @fabiantheis.bsky.social, and Barbara Treutlein for all their guidance and support! π
This work is part of the Human Cell Atlas effort to map all human cells. Explore more @natureportfolio.bsky.social: www.nature.com/immersive/d4...β¨
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A huge thank you to all collaborators: @chatgtp.bsky.social , @katelynxli.bsky.social, Irena SliΕ‘koviΔ, Hsiu-Chuan Lin, Malgorzata Santel, Alexander Atamian, @giorgiaquadrato.bsky.social, Jieran Sun, @sergiuppasca.bsky.social & the Human Cell Atlas Organoid Biological Network. π
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Contextualising New Data πΊ: The atlas serves as a powerful tool for annotating cell types, comparing protocols, and contextualising your new neural organoid scRNA-seq dataset. We provide a Python package (github.com/devsystemsla...) and interactive web interface (through www.archmap.bio#/).β¨
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Disease Modeling π· : Our atlas can be leveraged as a diverse control cohort to contextualise organoid models of disease, helping identify underlying genes and pathways. We found that comprehensive control data is vital for disentangling disease from regional effects.β¨
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Quantifying Organoid Fidelity π: By estimating the transcriptomic similarity between primary and organoid brain counterparts, we provide a robust framework for assessing protocol variation and fidelity across labs and methods. Cell stress seems to be quite dependent on protocol.β¨
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Identifying Missing Cell States π: We map neural organoid cell types and states to a developing human brain reference, highlighting gaps in the diversity of brain regions present in current in vitro models. β οΈWhile the telencephalon seems well-captured, this is not true for all brain regions.β¨
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Introducing the Human Neural Organoid Cell Atlas πΊοΈ: Weβve integrated 36 neural organoid datasets across 26 protocols. This produced a comprehensive, integrated reference atlas, including a hierarchical cell-type annotation, partially derived by mapping to primary human π§ .
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Super excited to share our Human Neural Organoid Atlas, now out in Nature! Led by @zhisonghe.bsky.social @josch1.bsky.social, and myself, this resource was created from 36 scRNA-seq datasetsβtotalling over 1.7 million cells! π¬β¨β¨ www.nature.com/articles/s41...β¨
Find out how it can serve you β¬β¨
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